# Iroko: A Framework to Prototype Reinforcement Learning for Data Center Traffic Control

arXiv: https://arxiv.org/abs/1812.09975 · HTML: https://mlsanity.com/paper/1812.09975/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Dec 2018.

## Built on by (most cited first, 3 of 3)

- [Glia: A Human-Inspired AI for Automated Systems Design and Optimization](https://mlsanity.com/paper/2510.27176/) (2025)
- [Learning Rewards to Optimize Global Performance Metrics in Deep Reinforcement Learning](https://mlsanity.com/paper/2303.09027/) (2023)
- [Machine Learning Empowered Intelligent Data Center Networking: A Survey](https://mlsanity.com/paper/2202.13549/) (2022)

## Abstract (arXiv)

Recent networking research has identified that data-driven congestion control (CC) can be more efficient than traditional CC in TCP. Deep reinforcement learning (RL), in particular, has the potential to learn optimal network policies. However, RL suffers from instability and over-fitting, deficiencies which so far render it unacceptable for use in datacenter networks. In this paper, we analyze the requirements for RL to succeed in the datacenter context. We present a new emulator, Iroko, which we developed to support different network topologies, congestion control algorithms, and deployment scenarios. Iroko interfaces with the OpenAI gym toolkit, which allows for fast and fair evaluation of different RL and traditional CC algorithms under the same conditions. We present initial benchmarks on three deep RL algorithms compared to TCP New Vegas and DCTCP. Our results show that these algorithms are able to learn a CC policy which exceeds the performance of TCP New Vegas on a dumbbell and fat-tree topology. We make our emulator open-source and publicly available: https://github.com/dcgym/iroko